AI-Driven Discovery of Highly Specific and Efficacious hCES2A Inhibitors for Ameliorating Irinotecan-Triggered Gut Toxicity
Zhipei Sang, Ya Zhang, Yufan Fan, Changhai Luan, Zhengwei Liu, Qiyao Zhang, Hairong Zeng, Yun‐Qing Song, Shuheng Huang, Guang‐Bo Ge
Abstract
The anticancer agent irinotecan often induces severe delayed-onset diarrhea, inhibiting human carboxylesterase 2A (hCES2A) can significantly alleviate irinotecan-triggered gut toxicity (ITGT). This work presents an efficient workflow for de novo design and developing novel efficacious hCES2A inhibitors. A well-training machine learning model identified scaffold-14 as a lead compound, while compound 14n was developed as a novel time-dependent hCES2A inhibitor (IC 50 = 0.04 nM) following three rounds of structural optimization. The covalent binding modes and inactivation mechanisms of 14n were elucidated by nanoLC-MS/MS-based chemoproteomics and covalent docking simulations. Notably, 14n showed excellent selectivity, good cell-membrane permeability, favorable drug-like properties, and potent inhibition on intracellular hCES2A. In vivo tests demonstrated that 14n was orally active, showing favorable safety profiles and impressive ameliorative effects on ITGT in tumor-bearing mice. Collectively, this work showcases a high-efficient AI-driven strategy for developing novel efficacious hCES2A inhibitors, while 14n emerges as a promising candidate for alleviating ITGT.